Adaptive or smart antennas for base stations have recently become recognized as a powerful tool for capacity and data-rates enhancement, mainly because of their co-channel interference (CCI) rejection capability. Usually, timeslot synchronization is assumed between the desired signal and interference. Known antenna array processing techniques can be applied in that case, see for example, Z. Zvonar, P. Jung, L. Kammerlander, (Editors), “GSM evolution towards 3rd generation systems”, Kluwer Academic Publishers, Boston/Dordreht/London, 1999. This assumption of time slot synchronisation between the desired signal and interference is valid if the neighboring base stations are synchronized and the cells are small. If unsynchronized base stations or large cells are employed, timeslot synchronization between signals is challenging. FIG. 1 shows a typical short burst scenario with (a) synchronous co-channel interference (CCI) and (b) asynchronous co-channel interference (CCI). The desired signal and two-component co-channel interference (CCI) bursts are plotted. In the asynchronous case the co-channel interference (CCI) bursts arrive with the random delay gi,i=1, . . . , M, where M is the number of the co-channel interference (CCI) components. In the synchronous case all bursts arrive simultaneously, i.e. gi=0,i=1, . . . , M. Random change of the interference structure during the desired signal burst causes significant performance degradation for known algorithms in the asynchronous case. Such known algorithms are described in the Zvonar reference mentioned above, and also J. Karlsson, “Adaptive antennas in GSM systems with non-synchronized base stations”, Licentiate's thesis, Dept. of Signals, Sensors and Systems, Royal Inst. of Technology, Sweden, 1997, and also E. Villier, L. Lopes, S. Aftelak, “On the application of uplink optimum combining to base station reception”, in Proc. IEEE 48th VTC, pp. 747-752, Ottawa, 1998.
It has been pointed out in M. C. Wells, “Increasing the capacity of GSM cellular radio using adaptive antennas”, IEE Proc. Communications, 1996, vol. 143, no. 5, pp. 304-310, that a stationary Space-Time Filter (STF) can be used to equalize the desired signal and reject the asynchronous co-channel interference (CCI) if the dimension of the space time filter (STF) is large enough; where stationary in this context means weight coefficients are fixed over a burst received under stationary propagation channels. The problem is that the known training based weight estimation algorithms, e.g. a Least Squares (LS) estimator, may not be effective because of the burst structure when the training sequence is concentrated in one part of the burst, e.g. the midambles of bursts in systems in accordance with Global System for Mobiles (GSM) or EDGE telecommunications standards, or preambles of bursts in systems in accordance with HIPERLAN/2 telecommunications standard. The GSM midamble case is shown in FIG. 1. One can see from FIG. 1(b) that, for GSM bursts, the training sequence of the desired signal may not even partially overlap with some of the co-channel interference (CCI) components due to other bursts.
One possible solution, which is proposed in the Wells paper mentioned above, is based on using the semi-blind algorithm with projections to the finite alphabet (FA), in other words selection of which of the finite number of symbols (e.g. 2 in a binary modulation scheme, 4 in a Quadrature Phase Shift Keying (QPSK) modulation scheme) was intended. Finite alphabet (FA) projection involves the whole timeslot of the desired signal and can be used for adjusting coefficients of a space time filter (STF) in the asynchronous case. Other semi-blind techniques e.g. based on the Constant Modulus property of the desired signal can also be exploited as described in , A. M. Kuzminskiy, P. Strauch, “Space-time filtering with suppression of asynchronous co-channel interference”, in Proc. Symposium 2000: Adaptive Syst. for Signal Proc., Commun., and Control, Lake Louise, October. 2000, and European Patent Publication EP-A-1100211.
FIG. 2 shows the structure of the receiver known from the Wells paper mentioned above with the following notations:
A is a receive antenna of K elements,
LST(which denotes Least Squares estimation over Training data) is the least squares (LS) estimator of the initial space time filter (STF) weight vector over the training interval of the burst, and
STF-LSP(I0) is the space time filter (STF) adjusted by means of the least squares (LS) algorithm with projections (LSP) to the finite alphabet (FA), where I0 is the number of iterations.
The estimator 10 in FIG. 2 works as follows. A multiple element antenna A, 12 receives the received signal 14 which is an additive mixture of the desired signal and the co-channel interference (CCI). The symbol sampled received signal is collected into data matrix X. The LST estimator block 16 is provided with training data and estimates the weight vectorŴLST={circumflex over (R)}XtXt−1{circumflex over (P)}StXt,  (Equation 1)
where {circumflex over (R)}XtXt and {circumflex over (P)}StXt are the correlation matrices of the received signal and the cross correlation vector of the desired and received signals estimated over the training interval, St is the vector of the training data and Xt is the input data matrix corresponded to the training interval (sub-matrix of matrix X). These initialising estimates 18 are provided to the space time filter-least squares estimator with projections (STF-LSP(I0)) estimator block 20 which iteratively estimates the vectors of the desired signal based on the least squares (LS) estimation of the weights over the whole burst:Ŝj=Q{XŴj−1},  (Equation 2)Ŵj={circumflex over (R)}XX−1{circumflex over (P)}ŜjX, j=1, . . . , I0,  (Equation 3)
where Q is a projector to the finite alphabet (FA) (slicer), I0 is the number of iterations and Ŵ0=ŴLST, i.e. the output of the LST estimator block 16 is used for the initialization of STF-LSP(I0) as shown in FIG. 2.
The disadvantage of such LST initialization is that it may suffer from insufficient amount of training data overlapping with the asynchronous co-channel interference (CCI) leading to the performance degradation of the iterative receiver in FIG. 2, especially in situations with no overlapping at all.
It has been noted, for example in the Zvonar and Villier papers referred to above, that the training data is not required for estimation of the correlation matrix in Equation 1. Thus the correlation matrix can be calculated over the whole burst of the received signal leading to the modified burst-based estimator (mentioned in the Zvonar paper referred to above)as follows:ŴLSB={circumflex over (R)}XX−1{circumflex over (P)}StXt.  (Equation 4)
This initialisation according to Equation 4 is included in a further known iterative receiver 10′ which is shown in FIG. 3. The FIG. 3 system is similar to the one shown in FIG. 2 except as regards the initialization. An advantage of this initialization is that it always contains information about the interference even if there is no overlapping with the training interval of the desired signal. A disadvantage of this solution is that the known least squares (LS) estimator of Equation 1 strictly and significantly outperforms the estimator defined by Equation 4 in the case that the interfering bursts overlap the training data in the signal burst (see for example the references in A. M. Kuzminskiy, “Finite amount of data effects in spatio-temporal filtering for equalisation and interference rejection in short burst wireless communications”, Signal Processing, Elsevier, vol. 80, no. 10, pp. 1987-1997, October 2000.).
In the asynchronous scenario illustrated in FIG. 1(b) any combination of temporal positions of the desired signal and co-channel interference (CCI) can occur on random basis. Thus, a fixed receiver as shown in FIG. 2 or FIG. 3 may not be suitable for some received bursts of data.